| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止（「運ぶ」「効く」「開かれた問い」など）、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。 |
| license | Unlicense（https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063） |
日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Internet connection and DNS routing are broken from WSL2 instances, when some VPNs are active.
The root cause seems to be that WSL2 and the VPN use the same IP address block, and the VPN routing clobbers WSL2's network routing.
This problem is tracked in multiple microsoft/WSL issues including, but not limited to:
Charts are from different sources and thus colors are inconsistent, please carefully read the chart's legends.
Similar analysis:
After all those years maintaining this data (since 2018): React is still much more popular and continue to grow faster than alternatives; Vue dates back from 2014 and Svelte from 2016, they should have overthrown React by now.
For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.
After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft
Python is the primary language in which TensorFlow models are typically developed and trained. TensorFlow does have bindings for other programming languages. These bindings have the low-level primitives that are required to build a more complete API, however, lack much of the higher-level API richness of the Python bindings, particularly for defining the model structure.
This file demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in C++.
| 更新: | 2026-06-08 |
|---|---|
| 作者: | @voluntas |
| バージョン: | 2026.1 |
| URL: | https://voluntas.github.io/ |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
